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Dissertation
A comparison of different machine learning methods in the prediction of college readiness: an exploratory study
University of Iowa
Doctor of Philosophy (PhD), University of Iowa
Summer 2025
DOI: 10.25820/etd.008086
Abstract
This study employs a machine learning approach for students’ college admissions. Specifically, it utilizes five machine learning models, including Linear Discriminant Analysis (LDA), K-Nearest Neighbor (KNN), Decision Tree, Random Forest, and Naïve Bayes Model, to predict students' admissions. Using the ACT dataset, this study presents analyses of machine learning models employing five different models. The findings indicate that all five models perform well overall, with the Random Forest model demonstrating the highest accuracy. The bootstrapping results illustrate that all five models are robust, exhibiting small standard deviations. While the accuracy rankings change slightly with different cut scores, the best- and worst-performing models remain consistent across cut scores. In general, the ACT score is the most significant factor in college admission. However, discrepancies exist between the model-generated predictions and the weights determined by the operational RAI index. This study seeks to enhance the admissions process by applying machine learning models, offering a more accurate and robust approach through predictive modeling.
Details
- Title: Subtitle
- A comparison of different machine learning methods in the prediction of college readiness: an exploratory study
- Creators
- Jeongmin Ji
- Contributors
- Catherine Welch (Advisor)Stephen Dunbar (Advisor)Won-Chan Lee (Committee Member)Kathy Schuh (Committee Member)
- Resource Type
- Dissertation
- Degree Awarded
- Doctor of Philosophy (PhD), University of Iowa
- Degree in
- Psychological and Quantitative Foundations (Educational Measurement and Statistics)
- Date degree season
- Summer 2025
- DOI
- 10.25820/etd.008086
- Publisher
- University of Iowa
- Number of pages
- vii, 105 pages
- Copyright
- Copyright 2025 Jeongmin Ji
- Language
- English
- Date submitted
- 05/13/2025
- Description illustrations
- illustrations, graphs, tables
- Description bibliographic
- Includes bibliographical references (pages 96-105).
- Public Abstract (ETD)
This study begins with the question of how machine learning approaches can support college readiness across various dimensions. This study aims to contribute to the understanding of students and educators by exploring more robust and accurate prediction models using machine learning approaches. With the increasing use of artificial intelligence, prediction models based on machine learning methods can help accurately measure college readiness. Using five machine learning methods, this study investigates the factors influencing college readiness and compares model performance to identify the most accurate predictor of students college readiness. Findings suggest that the performance of machine learning models is generally high, and some features are more important in predicting college admission compared to other features. Based on the findings, this study aims to support students better academic preparation for college admission and educators resource distribution before students college enrollment.
- Academic Unit
- Psychological and Quantitative Foundations
- Record Identifier
- 9984948239402771
Metrics
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